Deep Learning-Based Ligand Design Using Shared Latent Implicit Fingerprints from Collaborative Filtering.

In their previous work, Srinivas et al. [ 2018, 10, 56] have shown that implicit fingerprints capture ligands and proteins in a shared latent space, typically for the purposes of virtual screening with collaborative filtering models applied on known bioactivity data. In this work, we extend these implicit fingerprints/descriptors using deep learning techniques to translate latent descriptors into discrete representations of molecules (SMILES), without explicitly optimizing for chemical properties.

Implicit-descriptor ligand-based virtual screening by means of collaborative filtering.

Current ligand-based machine learning methods in virtual screening rely heavily on molecular fingerprinting for preprocessing, i.e., explicit description of ligands' structural and physicochemical properties in a vectorized form.